@inproceedings{
 title = {Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations},
 type = {inproceedings},
 year = {2020},
 pages = {439-448},
 websites = {http://arxiv.org/abs/1909.13340},
 month = {3},
 publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
 day = {20},
 id = {fc4ff380-f9ff-3388-8b55-902ff974390c},
 created = {2020-04-21T20:10:55.219Z},
 accessed = {2020-04-21},
 file_attached = {true},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2020-05-11T14:43:31.838Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 citation_key = {Fox2020},
 private_publication = {false},
 abstract = {We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates.},
 bibtype = {inproceedings},
 author = {Fox, Geoffrey and Jha, Shantenu},
 doi = {10.1109/escience.2019.00057}
}

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